Synthetic aperture radar (SAR) images are generated by transmitting electromagnetic waves as pulses. These pulses are reflected by a target. Received pulses are coherently integrated.
Because of the nature of data acquisition, SAR images are inherently affected by speckle noise and artifacts. Speckle noise is caused by coherent processing of backscattered signals from multiple distributed targets. Speckle noise is often modeled as multiplicative noise asyi=xi*zi,  (1)where yi, xi and zi correspond to the noisy, original, and noise intensities of the ith pixel, respectively.
Some methods use a logarithmic (log) transform to convert the multiplicative noise to an additive representation, and then apply additive noise filtering. Those methods are based on either wavelet shrinkage, global local averages, or dictionary learning.
The wavelet shrinkage uses a fixed complete (decimated and undecimated) wavelet bases on a log-intensity image where the wavelet transform coefficients are thresholded in an adaptive fashion to remove high frequency noise.
Instead of using fixed complete bases, another method extracts a global over-complete dictionary from the data and uses a sparse representation to suppress the incoherent noise. Even though that method may generate better results for Gaussian distributed noise in comparison to wavelet shrinkage techniques, the use of a single over-complete dictionary causes over-smoothing, especially when input and reconstruction blending parameter are not preset accurately.
A probabilistic patch based (PPB) filter uses a nonlocal means approach, and poses the problem of denoising as a weighted maximum likelihood estimation problem.
A nonlocal means method obtains a pixel estimate by determining a weighted average of all similar patches in the image. Those weights are directly proportional to the similarity of the patch surrounding a reference pixel and the patch surrounding candidate pixels in a search window. The weights are refines iteratively by including patches from the estimate of the image parameters. PPB can cope with non-Gaussian distributed noise, however, PPB also removes thin and dark details in the images.